{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "import scipy\n",
    "from scipy.sparse import csr_matrix, lil_matrix, hstack, vstack\n",
    "import scipy.sparse as sprs\n",
    "from tqdm.auto import tqdm\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sex, age, political preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl_1_1_dem = np.load('raw_data/cl_1_1_dem.npy')\n",
    "cl_1_2_dem = np.load('raw_data/cl_1_2_dem.npy')\n",
    "\n",
    "cl_2_dem = np.load('raw_data/cl_2_dem.npy')\n",
    "\n",
    "cl_3_dem = np.load('raw_data/cl_3_dem.npy')\n",
    "\n",
    "cl_4_1_dem = np.load('raw_data/cl_4_1_dem.npy')\n",
    "cl_4_2_dem = np.load('raw_data/cl_4_2_dem.npy')\n",
    "cl_4_3_dem = np.load('raw_data/cl_4_3_dem.npy')\n",
    "\n",
    "dem = np.vstack((cl_1_1_dem, cl_1_2_dem, \n",
    "                 cl_2_dem, \n",
    "                 cl_3_dem, \n",
    "                 cl_4_1_dem, cl_4_2_dem, cl_4_3_dem))\n",
    "\n",
    "del(cl_1_1_dem, cl_1_2_dem, cl_2_dem, cl_3_dem, cl_4_1_dem, cl_4_2_dem, cl_4_3_dem)\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Political Preferences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex Age Political Preferences\n",
       "0   1  30                    -1\n",
       "1   1  35                    -1\n",
       "2   2  -1                    -1\n",
       "3   2  -1                     1\n",
       "4   2  23                     2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem = pd.DataFrame(dem)\n",
    "dem = dem.rename(columns={0: 'Sex', \n",
    "                          1: 'Age', \n",
    "                          2: 'Political Preferences'})\n",
    "dem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem = dem.astype(float)\n",
    "\n",
    "#dem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    286316\n",
       "1.0    130141\n",
       "0.0       821\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0     264328\n",
       " 18.0      6980\n",
       " 19.0      6349\n",
       " 28.0      6210\n",
       " 30.0      5747\n",
       "          ...  \n",
       " 87.0        26\n",
       " 85.0        23\n",
       " 89.0        22\n",
       " 86.0        22\n",
       " 94.0        19\n",
       "Name: Age, Length: 104, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    417278.000000\n",
       "mean         11.715346\n",
       "std          19.712073\n",
       "min          -1.000000\n",
       "25%          -1.000000\n",
       "50%          -1.000000\n",
       "75%          24.000000\n",
       "max         116.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1      377482\n",
       "3        14979\n",
       "4         5856\n",
       "1         4958\n",
       "2         4566\n",
       "6         3001\n",
       "8         2665\n",
       "5         1798\n",
       "9         1005\n",
       "7          963\n",
       "666          3\n",
       "10           1\n",
       "1337         1\n",
       "Name: Political Preferences, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem['Political Preferences'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaps in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users who do not fill their sex: 0.0019675132645382695\n"
     ]
    }
   ],
   "source": [
    "print('Number of users who do not fill their sex:', dem[dem['Sex'] == '0'].shape[0]/dem.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users who do not fill their age: 0.6334577907294417\n"
     ]
    }
   ],
   "source": [
    "print('Number of users who do not fill their age:', dem[dem['Age'] == '-1'].shape[0]/dem.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users who do not fill their political preferences: 0.9046295275571681\n"
     ]
    }
   ],
   "source": [
    "print('Number of users who do not fill their political preferences:', \n",
    "      dem[dem['Political Preferences'] == '-1'].shape[0]/dem.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem[['Sex', 'Age', 'Political Preferences']] = dem[['Sex', \n",
    "                                                    'Age', \n",
    "                                                    'Political Preferences']].apply(pd.to_numeric)\n",
    "matplotlib.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec84d99b00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dem[dem['Sex'] != 0]['Sex'], \n",
    "             color='k', \n",
    "             kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec6366a5c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dem[dem['Age'] != -1]['Age'], \n",
    "             color='k', \n",
    "             kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_pp = ['коммунистические', 'социалистические', 'умеренные', \n",
    "              'либеральные', 'консервативные', 'монархические', \n",
    "              'ультраконсервативные', 'индифферентные', 'либертарианские']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_pp = ['communistic', 'socialistic', 'moderate', \n",
    "              'leberal', 'conservative', 'monarchical', \n",
    "              'ultra conservative', 'indifferent', 'libertarian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.rcParams.update({'font.size': 10})\n",
    "dem_temp = dem[dem['Political Preferences'] < 10]\n",
    "sns.distplot(dem_temp[dem_temp['Political Preferences'] != -1]['Political Preferences'], kde=False, bins=30, color=\"k\")\n",
    "\n",
    "plt.xticks(list(range(1, 10)), \n",
    "           list_of_pp, rotation=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec635b9048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dem_temp = dem[dem['Political Preferences'] < 10]\n",
    "sns.distplot(dem_temp[dem_temp['Political Preferences'] != -1]['Political Preferences'], \n",
    "             color='k', \n",
    "             kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Pyramide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Political Preferences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Age  Political Preferences\n",
       "0    1   30                     -1\n",
       "1    1   35                     -1\n",
       "2    2   -1                     -1\n",
       "3    2   -1                      1\n",
       "4    2   23                      2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 116\n"
     ]
    }
   ],
   "source": [
    "min_value = dem[dem['Age'] > 0]['Age'].min()\n",
    "max_value = dem[dem['Age'] > 0]['Age'].max()\n",
    "\n",
    "print(min_value, max_value)\n",
    "\n",
    "hist_male = np.histogram(dem[(dem['Age'] > 0) & (dem['Sex'] == 2)]['Age'], bins=10, \n",
    "                         range=(min_value, max_value))\n",
    "\n",
    "hist_female = np.histogram(dem[(dem['Age'] > 0) & (dem['Sex'] == 1)]['Age'], bins=10, \n",
    "                           range=(min_value, max_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36474 36990 18667  7805  4880  2489   620   252   936  1416]\n",
      "[13733 13110  5727  3635  3411  1588   286    69   236   360]\n",
      "[ 14.   24.2  34.4  44.6  54.8  65.   75.2  85.4  95.6 105.8 116. ]\n"
     ]
    }
   ],
   "source": [
    "males = hist_male[0]\n",
    "females = hist_female[0]\n",
    "ages = hist_male[1]\n",
    "print(males)\n",
    "print(females)\n",
    "print(hist_male[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['14-24',\n",
       " '25-34',\n",
       " '35-44',\n",
       " '45-54',\n",
       " '55-65',\n",
       " '66-75',\n",
       " '76-85',\n",
       " '86-95',\n",
       " '96-105',\n",
       " '106-116']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_intervales = []\n",
    "for i in range(len(ages) - 1):\n",
    "    if i == 0:\n",
    "        age_intervales.append(str(int(ages[i])) + '-' + str(int(ages[i+1])))\n",
    "    else:\n",
    "        age_intervales.append(str(int(ages[i])+1) + '-' + str(int(ages[i+1])))\n",
    "age_intervales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_intervales</th>\n",
       "      <th>males</th>\n",
       "      <th>females</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14-24</td>\n",
       "      <td>-36474</td>\n",
       "      <td>13733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-34</td>\n",
       "      <td>-36990</td>\n",
       "      <td>13110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35-44</td>\n",
       "      <td>-18667</td>\n",
       "      <td>5727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45-54</td>\n",
       "      <td>-7805</td>\n",
       "      <td>3635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55-65</td>\n",
       "      <td>-4880</td>\n",
       "      <td>3411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_intervales  males  females\n",
       "0          14-24 -36474    13733\n",
       "1          25-34 -36990    13110\n",
       "2          35-44 -18667     5727\n",
       "3          45-54  -7805     3635\n",
       "4          55-65  -4880     3411"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pyr_data = pd.DataFrame(data={'age_intervales': age_intervales, 'males' : -males, 'females' : females})\n",
    "Pyr_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "bar_plot = sns.barplot(x=\"males\",y=\"age_intervales\", color=\"red\", label=\"Males\",data = Pyr_data)\n",
    "\n",
    "bar_plot = sns.barplot(x=\"females\",y=\"age_intervales\", color=\"blue\", label=\"Females\",data = Pyr_data)\n",
    "\n",
    "bar_plot.set(xlabel=\"Number of Users\", ylabel=\"Age Group\");\n",
    "\n",
    "plt.xticks([-30000, -20000, -10000, 0, 10000], \n",
    "           [30000, 20000, 10000, 0, 10000])\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subscriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sub = sprs.load_npz('encoded_data/X_sub_full.npz')\n",
    "sums = X_sub.sum(axis=1)\n",
    "sums = np.array(sums).ravel()\n",
    "sums = pd.Series(sums, name=\"Number of Subscroptions k\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "sns.distplot(sums[sums < 201],\n",
    "             color='k', \n",
    "             kde=False)\n",
    "\n",
    "plt.xlabel('Number of Subscriptions $k$', fontsize = 10)\n",
    "plt.ylabel('Number of Users Having $k$ Subscriptions', fontsize = 10)\n",
    "\n",
    "plt.yticks([20000, 40000, 60000, 80000, 100000], \n",
    "           [format(20000, \"10.0E\"), format(40000, \"10.0E\"), \n",
    "            format(60000, \"10.0E\"), format(80000, \"10.0E\"), format(100000, \"10.0E\")])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users with no subscriptions on publics and blogers: 0.2211643077277019\n"
     ]
    }
   ],
   "source": [
    "print('Number of users with no subscriptions on publics and blogers:', \n",
    "      sums[sums == 0].shape[0] / sums.shape[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
